# AutoBag: Learning to Open Plastic Bags and Insert Objects

arXiv: https://arxiv.org/abs/2210.17217 · HTML: https://mlsanity.com/paper/2210.17217/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2022.

## Built on by (most cited first, 14 of 14)

- [ManipBench: Benchmarking Vision-Language Models for Low-Level Robot Manipulation](https://mlsanity.com/paper/2505.09698/) (2025)
- [UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondence](https://mlsanity.com/paper/2405.06903/) (2024)
- [Challenges for Monocular 6D Object Pose Estimation in Robotics](https://mlsanity.com/paper/2307.12172/) (2023)
- [VoxAct-B: Voxel-Based Acting and Stabilizing Policy for Bimanual Manipulation](https://mlsanity.com/paper/2407.04152/) (2024)
- [DeformPAM: Data-Efficient Learning for Long-horizon Deformable Object Manipulation via Preference-based Action Alignment](https://mlsanity.com/paper/2410.11584/) (2024)
- [DiffVL: Scaling Up Soft Body Manipulation using Vision-Language Driven Differentiable Physics](https://mlsanity.com/paper/2312.06408/) (2023)
- [D-CODA: Diffusion for Coordinated Dual-Arm Data Augmentation](https://mlsanity.com/paper/2505.04860/) (2025)
- [SkillVLA: Tackling Combinatorial Diversity in Dual-Arm Manipulation via Skill Reuse](https://mlsanity.com/paper/2603.03836/) (2026)
- [Non-rigid Relative Placement through 3D Dense Diffusion](https://mlsanity.com/paper/2410.19247/) (2024)
- [RoboVista: Evaluating Vision Language Models for Diverse Robot Applications](https://mlsanity.com/paper/2607.04610/) (2026)
- [Learning to bag with a simulation-free reinforcement learning framework for robots](https://mlsanity.com/paper/2310.14398/) (2023)
- [Automating Deformable Gasket Assembly](https://mlsanity.com/paper/2408.12593/) (2024)
- [Dynamic Manipulation of Deformable Objects using Imitation Learning with Adaptation to Hardware Constraints](https://mlsanity.com/paper/2403.12685/) (2024)
- [DexKnot: Generalizable Visuomotor Policy Learning for Dexterous Bag-Knotting Manipulation](https://mlsanity.com/paper/2603.07136/) (2026)

## Abstract (arXiv)

Thin plastic bags are ubiquitous in retail stores, healthcare, food handling, recycling, homes, and school lunchrooms. They are challenging both for perception (due to specularities and occlusions) and for manipulation (due to the dynamics of their 3D deformable structure). We formulate the task of "bagging:" manipulating common plastic shopping bags with two handles from an unstructured initial state to an open state where at least one solid object can be inserted into the bag and lifted for transport. We propose a self-supervised learning framework where a dual-arm robot learns to recognize the handles and rim of plastic bags using UV-fluorescent markings; at execution time, the robot does not use UV markings or UV light. We propose the AutoBag algorithm, where the robot uses the learned perception model to open a plastic bag through iterative manipulation. We present novel metrics to evaluate the quality of a bag state and new motion primitives for reorienting and opening bags based on visual observations. In physical experiments, a YuMi robot using AutoBag is able to open bags and achieve a success rate of 16/30 for inserting at least one item across a variety of initial bag configurations. Supplementary material is available at https://sites.google.com/view/autobag.